1. Applicability of CMAQ-DDM to source apportionment and control strategy development Daniel Cohan Georgia Institute of Technology 2004 Models-3 Users’ Workshop October 19, 2004

2. Policy Applications of AQ Models Source apportionment Source apportionment How much of ambient pollutant concentrations can be attributed to each emission source? How much of ambient pollutant concentrations can be attributed to each emission source? Control strategy assessment Control strategy assessment How would concentrations change under a control measure? How would concentrations change under a control measure? Forward projections Forward projections How will concentrations change under future trends (regulation, technology, growth)? How will concentrations change under future trends (regulation, technology, growth)?

3. Concentrations Emissions, Initial Conditions, Boundary Conditions, etc. Air Quality Model ∆ Sensitivities Check scientific understanding Extend beyond observations Forecasting and prediction ∆ (e.g., Atlanta Emissions) Air Quality Model Atmospheric response Control strategies Source apportionment

4. E VOC E NOx Low O 3 High O 3 Complication: Nonlinearity Often, only a handful of sensitivities are modeled (e.g., 30% NO x reduction, 30% VOC reduction) Often, only a handful of sensitivities are modeled (e.g., 30% NO x reduction, 30% VOC reduction) Linear scaling and additivity assumption may be inaccurate Linear scaling and additivity assumption may be inaccurate But it may be impractical to model all combinations of emission sources or control measures But it may be impractical to model all combinations of emission sources or control measures

5. E VOC Ozone -EA-EA  ∆C∆C EAEAEAEA CACACACA Brute Force and HDDM-3D CBCBCBCB EBEBEBEB B A + +

6. Applications of HDDM-3D Incremental sensitivity First-order sensitivity S (1) = ∂C/∂  Taylor expansion (  <0) C ≈ C 0 +  S (1) + 0.5  2 S (2) Control strategy Source apportionment Taylor expansion (  =-1) S.C. ≈ S (1) - 0.5∙S (2)

7. Consistency of local sensitivities Brute Force HDDM-3D R 2 > 0.99 Low bias & error

8. HDDM performance & nonlinearity For 8-hr ozone, averaged over 12-km domain, Aug. 13-19, 2000 (2007 emissions) 1 st +2 nd order: Well captures response 1 st order only: Extent of nonlinearity DDM – Brute Force % emission reduction

9. Interactions of emission impacts 1 st -order Cross term 2 nd -order 1 st -order 2 nd -order Impact of single perturbation: E(x,t)=E 0 (x,t)+ε j p j (x,t) Impact of dual perturbation: E(x,t)=E 0 (x,t)+ε j p j (x,t)+ε k p k (x,t)

10. MaconAtlanta August 17, 2000 peak-hour ozone (from the method of Hakami et al., 2004) Ozone (ppmV) Isopleths of atmospheric response

11. Atlanta apportionment by NO x category Contribution to Atlanta ozone (ppm) Atlanta NO x : Aggregate Atlanta NO x : By Category Cross- sens. Sum of parts Atlanta MSA, 8-hour ozone, Aug. 13-19, 2000 (Year 2007 emissions)

12. Macon apportionment by NO x source region Macon MSA, Aug. 13-19, 2000 (2007 emissions) M A S B

13. Recommendations HDDM-3D is a powerful scoping tool for examining numerous source contributions or control measures, and the interactions among pairs HDDM-3D is a powerful scoping tool for examining numerous source contributions or control measures, and the interactions among pairs Caution: Only 1 st order DDM-3D is being implemented for PM Caution: Only 1 st order DDM-3D is being implemented for PM Due to nonlinearity and non-additivity, an aggregate brute force assessment should be used to evaluate the cumulative effect of the entire strategy Due to nonlinearity and non-additivity, an aggregate brute force assessment should be used to evaluate the cumulative effect of the entire strategy Iterative DDM-3D / brute force approach may be considered Iterative DDM-3D / brute force approach may be considered Important to match modeling methods to objectives in source apportionment and strategy assessment Important to match modeling methods to objectives in source apportionment and strategy assessment Contribution of aggregate emissions may differ from sum of parts Contribution of aggregate emissions may differ from sum of parts

14. Acknowledgments Funding: Fall-Line Air Quality Study (Georgia Environmental Protection Division and the Fall-Line cities) Funding: Fall-Line Air Quality Study (Georgia Environmental Protection Division and the Fall-Line cities) Amir Hakami, Yongtao Hu, Ted Russell Amir Hakami, Yongtao Hu, Ted Russell